MACHINE LEARNING MODEL FOR PREDICTING CROP YIELD PRODUCTION









Abstract

Data Mining is the greatest conceivable strategy for the predominant virtual global for perusing mass of records units to acquire left out relationship. The methodology utilized for the assessment of factual records over a time period is the time assortment assessment. This technique is clinical and reliable in determining events to see over a period. Likelihood of assembling ought to ceaselessly be relied upon to the near flawlessness with the guide of utilizing time assortment assessment. In this work, suppers fabricating is the pushed for forecast. The recognized sort systems on this investigate are the Linear Regression (LR) and Naive Bayes. In this venture, Linear Regression and Naive are the proposed outfit rendition used to task the yield producing over a time span. This troupe rendition is when contrasted with Linear Regression and Naive Bayes methodologies. The boundaries utilized each in turn for expectation of yield are the precision and the sort mistake. The finding yields that Linear Regression and Naive are pleasing than Linear Regression and Naive Bayes for the records set examined. Keywords: Data Mining, Linear Regression, Naïve Bayes, Likelihood, Yield Production.


Modules


Algorithms


Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL